Adding version check

- Using pkg_resources
- Test is done

hparams.py

@@ -184,7 +184,7 @@
 
 	#During synthesis, there is no max_time_steps limitation so the model can sample much longer audio than 8k(or 13k) steps. (Audio can go up to 500k steps, equivalent to ~21sec on 24kHz)
 	#Usually your GPU can handle 1x~2x wavenet_batch_size during synthesis for the same memory amount during training (because no gradients to keep and ops to register for backprop)
-	wavenet_synthesis_batch_size = 4 * 2, #This ensure that wavenet synthesis goes up to 4x~8x faster when synthesizing multiple sentences. Watch out for OOM with long audios.
+	wavenet_synthesis_batch_size = 2, #This ensure that wavenet synthesis goes up to 4x~8x faster when synthesizing multiple sentences. Watch out for OOM with long audios.
 
 	wavenet_learning_rate = 1e-3,
 	wavenet_adam_beta1 = 0.9,

tacotron/models/custom_decoder.py

@@ -10,7 +10,7 @@
 from tensorflow.python.layers import base as layers_base
 from tensorflow.python.ops import rnn_cell_impl
 from tensorflow.python.util import nest
-
+from pkg_resources import parse_version
 
 class CustomDecoderOutput(
 		collections.namedtuple("CustomDecoderOutput", ("rnn_output", "token_output", "sample_id"))):
@@ -40,11 +40,11 @@ def __init__(self, cell, helper, initial_state, output_layer=None):
 		Raises:
 			TypeError: if `cell`, `helper` or `output_layer` have an incorrect type.
 		"""
-		if (tf.__version__ < '1.10'):
+		if (parse_version(tf.__version__) >= parse_version('1.10')):
+			rnn_cell_impl.assert_like_rnncell(type(cell), cell)
+		else:
 			if not rnn_cell_impl._like_rnncell(cell):  # pylint: disable=protected-access
 				raise TypeError("cell must be an RNNCell, received: %s" % type(cell))
-		else:
-			rnn_cell_impl.assert_like_rnncell(type(cell), cell)
 		if not isinstance(helper, helper_py.Helper):
 			raise TypeError("helper must be a Helper, received: %s" % type(helper))
 		if (output_layer is not None